What You Will Learn

Use the Intel Distribution of OpenVINO toolkit to detect brain tumors in MRI images. A pretrained model from open source datasets helps accurately predict results using the Sørensen–Dice coefficient.

Gain insight into the following solutions:

  • Computer vision applications for healthcare
  • Computer vision inference for medical image processing

Learn to build and run an application with these capabilities:

Perform segmentation of MRI scans to detect brain tumors.


Calculate the Sørensen–Dice coefficient and plot the prediction results.


Save output images that show segmented areas in MRI scans.


How It Works

Using a combination of different computer vision techniques, this application performs brain tumor image segmentation on MRI scans and plots the Sørensen–Dice coefficient.

  1. Train the model using an open source dataset from the Medical Segmentation Decathlon for segmenting nerves in ultrasound images and lungs in computed tomography (CT) scans. More Information
  2. Use the model with the inference engine. Apply the results to calculate the Sørensen–Dice coefficient and to plot predictions from a segmented brain tumor.
  3. Store the output images from the segmented brain tumor locally.

flow chart graphic of how the brain tumor segmentation reference implementation works


What You Need

Software Requirements

Ubuntu 16.04 LTS (preinstalled on the hardware)

OpenCL™ Runtime package

 

Model Used

Custom Pretrained Model